Exploratory study of “real world” implementation of a clinical poverty tool in diverse family medicine and pediatric care settings
Bibliographic record
Abstract
BACKGROUND: Poverty is associated with increased morbidity related to multiple child and adult health conditions and increased risk of premature death. Despite robust evidence linking income and health, and some recommendations for universal screening, poverty screening is not routinely conducted in clinical care. METHODS: We conducted an exploratory study of implementing universal poverty screening and intervention in family medicine and a range of pediatric care settings (primary through tertiary). After attending a training session, health care providers (HCPs) were instructed to perform universal screening using a clinical poverty tool with the question "Do you ever have difficulty making ends meet at the end of the month?" for the three-month implementation period. HCPs tracked the number of patients screened and a convenience sample of their patients were surveyed regarding the acceptability of being screened for poverty in a healthcare setting. HCPs participated in semi-structured focus groups to explore barriers to and facilitators of universal implementation of the tool. RESULTS: Twenty-two HCPs (10 pediatricians, 9 family physicians, 3 nurse practitioners) participated and 150 patients completed surveys. Eighteen HCPs participated in focus groups. Despite the self-described motivation of the HCPs, screening rates were low (9% according to self-reported numbers). The majority of patients either supported (72%) or were neutral (22%) about the appropriateness of HCPs screening for and intervening on poverty. HCPs viewed poverty as relevant to clinical care but identified time constraints, physician discomfort, lack of expertise and habitual factors as barriers to implementation of universal screening. CONCLUSIONS: Poverty screening is important and acceptable to clinicians and patients. However, multiple barriers need to be addressed to allow for successful implementation of poverty screening and intervention in health care settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".